SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1625 of 15113 papers

TitleStatusHype
Scaling Multi-Agent Reinforcement Learning with Selective Parameter SharingCode1
LTL2Action: Generalizing LTL Instructions for Multi-Task RLCode1
Scalable Bayesian Inverse Reinforcement LearningCode1
Multi-Task Reinforcement Learning with Context-based RepresentationsCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Risk-Averse Offline Reinforcement LearningCode1
Domain Adaptation In Reinforcement Learning Via Latent Unified State RepresentationCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Reverb: A Framework For Experience ReplayCode1
rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching TasksCode1
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning WorkloadsCode1
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement LearningCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Explainable Reinforcement Learning for Longitudinal ControlCode1
LongiControl: A Reinforcement Learning Environment for Longitudinal Vehicle ControlCode1
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement LearningCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
NeoRL: A Near Real-World Benchmark for Offline Reinforcement LearningCode1
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Contextualized Rewriting for Text SummarizationCode1
Learning Synthetic Environments for Reinforcement Learning with Evolution StrategiesCode1
Differentiable Trust Region Layers for Deep Reinforcement LearningCode1
Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis TreatmentCode1
Robust Reinforcement Learning on State Observations with Learned Optimal AdversaryCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified